Overview

Dataset statistics

Number of variables63
Number of observations266
Missing cells15289
Missing cells (%)91.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.5 KiB
Average record size in memory617.9 B

Variable types

Categorical12
Unsupported26
Numeric25

Alerts

1985 has constant value "3.45915269851685" Constant
1986 has constant value "3.14806771278381" Constant
1987 has constant value "1.60447299480438" Constant
1988 has constant value "2.32686972618103" Constant
Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
1992 is highly correlated with 1997 and 7 other fieldsHigh correlation
1993 is highly correlated with 1998 and 12 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 12 other fieldsHigh correlation
1996 is highly correlated with 1998 and 9 other fieldsHigh correlation
1997 is highly correlated with 1992 and 20 other fieldsHigh correlation
1998 is highly correlated with 1992 and 23 other fieldsHigh correlation
1999 is highly correlated with 1993 and 23 other fieldsHigh correlation
2000 is highly correlated with 1993 and 23 other fieldsHigh correlation
2001 is highly correlated with 1993 and 24 other fieldsHigh correlation
2002 is highly correlated with 1992 and 23 other fieldsHigh correlation
2003 is highly correlated with 1993 and 24 other fieldsHigh correlation
2004 is highly correlated with 1992 and 25 other fieldsHigh correlation
2005 is highly correlated with 1993 and 23 other fieldsHigh correlation
2006 is highly correlated with 1993 and 25 other fieldsHigh correlation
2007 is highly correlated with 1993 and 22 other fieldsHigh correlation
2008 is highly correlated with 1992 and 23 other fieldsHigh correlation
2009 is highly correlated with 1993 and 23 other fieldsHigh correlation
2010 is highly correlated with 1993 and 23 other fieldsHigh correlation
2011 is highly correlated with 1994 and 23 other fieldsHigh correlation
2012 is highly correlated with 1992 and 23 other fieldsHigh correlation
2013 is highly correlated with 1995 and 23 other fieldsHigh correlation
2014 is highly correlated with 1992 and 25 other fieldsHigh correlation
2015 is highly correlated with 1994 and 24 other fieldsHigh correlation
2016 is highly correlated with 1994 and 23 other fieldsHigh correlation
2017 is highly correlated with 1994 and 22 other fieldsHigh correlation
2018 is highly correlated with 1992 and 24 other fieldsHigh correlation
2019 is highly correlated with 1997 and 17 other fieldsHigh correlation
2020 is highly correlated with 1997 and 16 other fieldsHigh correlation
1992 is highly correlated with 1997 and 7 other fieldsHigh correlation
1993 is highly correlated with 1998 and 11 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 12 other fieldsHigh correlation
1996 is highly correlated with 1997 and 14 other fieldsHigh correlation
1997 is highly correlated with 1992 and 22 other fieldsHigh correlation
1998 is highly correlated with 1992 and 22 other fieldsHigh correlation
1999 is highly correlated with 1993 and 21 other fieldsHigh correlation
2000 is highly correlated with 1993 and 21 other fieldsHigh correlation
2001 is highly correlated with 1993 and 23 other fieldsHigh correlation
2002 is highly correlated with 1992 and 25 other fieldsHigh correlation
2003 is highly correlated with 1993 and 23 other fieldsHigh correlation
2004 is highly correlated with 1992 and 25 other fieldsHigh correlation
2005 is highly correlated with 1993 and 24 other fieldsHigh correlation
2006 is highly correlated with 1993 and 24 other fieldsHigh correlation
2007 is highly correlated with 1993 and 22 other fieldsHigh correlation
2008 is highly correlated with 1992 and 22 other fieldsHigh correlation
2009 is highly correlated with 1994 and 21 other fieldsHigh correlation
2010 is highly correlated with 1993 and 24 other fieldsHigh correlation
2011 is highly correlated with 1994 and 23 other fieldsHigh correlation
2012 is highly correlated with 1992 and 24 other fieldsHigh correlation
2013 is highly correlated with 1995 and 22 other fieldsHigh correlation
2014 is highly correlated with 1992 and 25 other fieldsHigh correlation
2015 is highly correlated with 1994 and 23 other fieldsHigh correlation
2016 is highly correlated with 1994 and 23 other fieldsHigh correlation
2017 is highly correlated with 1994 and 24 other fieldsHigh correlation
2018 is highly correlated with 1992 and 22 other fieldsHigh correlation
2019 is highly correlated with 1997 and 17 other fieldsHigh correlation
2020 is highly correlated with 1997 and 16 other fieldsHigh correlation
1992 is highly correlated with 1997 and 7 other fieldsHigh correlation
1993 is highly correlated with 1998 and 11 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 8 other fieldsHigh correlation
1996 is highly correlated with 1998 and 3 other fieldsHigh correlation
1997 is highly correlated with 1992 and 18 other fieldsHigh correlation
1998 is highly correlated with 1992 and 17 other fieldsHigh correlation
1999 is highly correlated with 1993 and 19 other fieldsHigh correlation
2000 is highly correlated with 1993 and 16 other fieldsHigh correlation
2001 is highly correlated with 1993 and 22 other fieldsHigh correlation
2002 is highly correlated with 1992 and 22 other fieldsHigh correlation
2003 is highly correlated with 1993 and 19 other fieldsHigh correlation
2004 is highly correlated with 1992 and 22 other fieldsHigh correlation
2005 is highly correlated with 1993 and 23 other fieldsHigh correlation
2006 is highly correlated with 1993 and 23 other fieldsHigh correlation
2007 is highly correlated with 1993 and 19 other fieldsHigh correlation
2008 is highly correlated with 1992 and 23 other fieldsHigh correlation
2009 is highly correlated with 1994 and 22 other fieldsHigh correlation
2010 is highly correlated with 1993 and 23 other fieldsHigh correlation
2011 is highly correlated with 1994 and 21 other fieldsHigh correlation
2012 is highly correlated with 1992 and 22 other fieldsHigh correlation
2013 is highly correlated with 1995 and 21 other fieldsHigh correlation
2014 is highly correlated with 1992 and 23 other fieldsHigh correlation
2015 is highly correlated with 1994 and 22 other fieldsHigh correlation
2016 is highly correlated with 1994 and 17 other fieldsHigh correlation
2017 is highly correlated with 1994 and 21 other fieldsHigh correlation
2018 is highly correlated with 1992 and 19 other fieldsHigh correlation
2019 is highly correlated with 1997 and 16 other fieldsHigh correlation
2020 is highly correlated with 1997 and 15 other fieldsHigh correlation
1992 is highly correlated with 2002 and 1 other fieldsHigh correlation
1993 is highly correlated with 1998 and 2 other fieldsHigh correlation
1994 is highly correlated with 2011High correlation
1995 is highly correlated with 2000 and 9 other fieldsHigh correlation
1996 is highly correlated with 1998 and 9 other fieldsHigh correlation
1997 is highly correlated with 1998 and 16 other fieldsHigh correlation
1998 is highly correlated with 1993 and 19 other fieldsHigh correlation
1999 is highly correlated with 1996 and 20 other fieldsHigh correlation
2000 is highly correlated with 1995 and 19 other fieldsHigh correlation
2001 is highly correlated with 1993 and 21 other fieldsHigh correlation
2002 is highly correlated with 1992 and 21 other fieldsHigh correlation
2003 is highly correlated with 1995 and 19 other fieldsHigh correlation
2004 is highly correlated with 1992 and 22 other fieldsHigh correlation
2005 is highly correlated with 1997 and 21 other fieldsHigh correlation
2006 is highly correlated with 1995 and 21 other fieldsHigh correlation
2007 is highly correlated with 1995 and 22 other fieldsHigh correlation
2008 is highly correlated with 1996 and 21 other fieldsHigh correlation
2009 is highly correlated with 1993 and 21 other fieldsHigh correlation
2010 is highly correlated with 1995 and 24 other fieldsHigh correlation
2011 is highly correlated with 1994 and 22 other fieldsHigh correlation
2012 is highly correlated with 1996 and 22 other fieldsHigh correlation
2013 is highly correlated with 1995 and 23 other fieldsHigh correlation
2014 is highly correlated with 1996 and 22 other fieldsHigh correlation
2015 is highly correlated with 1995 and 21 other fieldsHigh correlation
2016 is highly correlated with 1995 and 22 other fieldsHigh correlation
2017 is highly correlated with 1995 and 20 other fieldsHigh correlation
2018 is highly correlated with 1999 and 17 other fieldsHigh correlation
2019 is highly correlated with 1996 and 16 other fieldsHigh correlation
2020 is highly correlated with 2002 and 4 other fieldsHigh correlation
1960 has 266 (100.0%) missing values Missing
1961 has 266 (100.0%) missing values Missing
1962 has 266 (100.0%) missing values Missing
1963 has 266 (100.0%) missing values Missing
1964 has 266 (100.0%) missing values Missing
1965 has 266 (100.0%) missing values Missing
1966 has 266 (100.0%) missing values Missing
1967 has 266 (100.0%) missing values Missing
1968 has 266 (100.0%) missing values Missing
1969 has 266 (100.0%) missing values Missing
1970 has 266 (100.0%) missing values Missing
1971 has 266 (100.0%) missing values Missing
1972 has 266 (100.0%) missing values Missing
1973 has 266 (100.0%) missing values Missing
1974 has 266 (100.0%) missing values Missing
1975 has 266 (100.0%) missing values Missing
1976 has 266 (100.0%) missing values Missing
1977 has 266 (100.0%) missing values Missing
1978 has 266 (100.0%) missing values Missing
1979 has 266 (100.0%) missing values Missing
1980 has 266 (100.0%) missing values Missing
1981 has 266 (100.0%) missing values Missing
1982 has 266 (100.0%) missing values Missing
1983 has 266 (100.0%) missing values Missing
1984 has 266 (100.0%) missing values Missing
1985 has 265 (99.6%) missing values Missing
1986 has 265 (99.6%) missing values Missing
1987 has 265 (99.6%) missing values Missing
1988 has 265 (99.6%) missing values Missing
1989 has 266 (100.0%) missing values Missing
1990 has 263 (98.9%) missing values Missing
1991 has 264 (99.2%) missing values Missing
1992 has 262 (98.5%) missing values Missing
1993 has 262 (98.5%) missing values Missing
1994 has 263 (98.9%) missing values Missing
1995 has 256 (96.2%) missing values Missing
1996 has 250 (94.0%) missing values Missing
1997 has 251 (94.4%) missing values Missing
1998 has 245 (92.1%) missing values Missing
1999 has 243 (91.4%) missing values Missing
2000 has 223 (83.8%) missing values Missing
2001 has 238 (89.5%) missing values Missing
2002 has 230 (86.5%) missing values Missing
2003 has 229 (86.1%) missing values Missing
2004 has 226 (85.0%) missing values Missing
2005 has 209 (78.6%) missing values Missing
2006 has 225 (84.6%) missing values Missing
2007 has 223 (83.8%) missing values Missing
2008 has 228 (85.7%) missing values Missing
2009 has 220 (82.7%) missing values Missing
2010 has 203 (76.3%) missing values Missing
2011 has 227 (85.3%) missing values Missing
2012 has 222 (83.5%) missing values Missing
2013 has 226 (85.0%) missing values Missing
2014 has 222 (83.5%) missing values Missing
2015 has 208 (78.2%) missing values Missing
2016 has 215 (80.8%) missing values Missing
2017 has 224 (84.2%) missing values Missing
2018 has 240 (90.2%) missing values Missing
2019 has 253 (95.1%) missing values Missing
2020 has 263 (98.9%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
1990 is uniformly distributed Uniform
1991 is uniformly distributed Uniform
1992 is uniformly distributed Uniform
1993 is uniformly distributed Uniform
1994 is uniformly distributed Uniform
2020 is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1960 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1961 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1962 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1963 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1964 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1965 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1966 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1967 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1968 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1969 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1970 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1971 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1972 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1973 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1974 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1975 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1976 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1977 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1978 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1979 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1980 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1981 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1982 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1983 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1984 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1989 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-03 16:45:03.242425
Analysis finished2022-04-03 16:46:08.107716
Duration1 minute and 4.87 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Aruba
 
1
Oman
 
1
Malawi
 
1
Malaysia
 
1
North America
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAruba
2nd rowAfrica Eastern and Southern
3rd rowAfghanistan
4th rowAfrica Western and Central
5th rowAngola

Common Values

ValueCountFrequency (%)
Aruba1
 
0.4%
Oman1
 
0.4%
Malawi1
 
0.4%
Malaysia1
 
0.4%
North America1
 
0.4%
Namibia1
 
0.4%
New Caledonia1
 
0.4%
Niger1
 
0.4%
Nigeria1
 
0.4%
Nicaragua1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:46:08.195482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
islands9
 
1.8%
africa9
 
1.8%
ibrd8
 
1.6%
asia8
 
1.6%
countries7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
ABW
 
1
OMN
 
1
MWI
 
1
MYS
 
1
NAC
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowABW
2nd rowAFE
3rd rowAFG
4th rowAFW
5th rowAGO

Common Values

ValueCountFrequency (%)
ABW1
 
0.4%
OMN1
 
0.4%
MWI1
 
0.4%
MYS1
 
0.4%
NAC1
 
0.4%
NAM1
 
0.4%
NCL1
 
0.4%
NER1
 
0.4%
NGA1
 
0.4%
NIC1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:46:08.307186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abw1
 
0.4%
aut1
 
0.4%
btn1
 
0.4%
brn1
 
0.4%
afg1
 
0.4%
afw1
 
0.4%
ago1
 
0.4%
alb1
 
0.4%
and1
 
0.4%
arb1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1960
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1961
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1962
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1963
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1964
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1965
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1966
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1967
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1968
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1969
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1970
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1971
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1972
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1973
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1974
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1975
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1976
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1977
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1978
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1979
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1980
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1981
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1982
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1983
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1984
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1985
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
3.45915269851685

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row3.45915269851685

Common Values

ValueCountFrequency (%)
3.459152698516851
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:46:08.410407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:08.462267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.459152698516851
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1986
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
3.14806771278381

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row3.14806771278381

Common Values

ValueCountFrequency (%)
3.148067712783811
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:46:08.778290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:08.840152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.148067712783811
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1987
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
1.60447299480438

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.60447299480438

Common Values

ValueCountFrequency (%)
1.604472994804381
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:46:08.901987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:08.961855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.604472994804381
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1988
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
2.32686972618103

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.32686972618103

Common Values

ValueCountFrequency (%)
2.326869726181031
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:46:09.023691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.084554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.326869726181031
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1989
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1990
Categorical

MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
2.14403510093689
1.80556535720825
0.438902854919434

Length

Max length17
Median length16
Mean length16.33333333
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row2.14403510093689
2nd row1.80556535720825
3rd row0.438902854919434

Common Values

ValueCountFrequency (%)
2.144035100936891
 
0.4%
1.805565357208251
 
0.4%
0.4389028549194341
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:46:09.150392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.220166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.144035100936891
33.3%
1.805565357208251
33.3%
0.4389028549194341
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1991
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing264
Missing (%)99.2%
Memory size10.6 KiB
0.598052203655243
2.36087679862976

Length

Max length17
Median length16.5
Mean length16.5
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row0.598052203655243
2nd row2.36087679862976

Common Values

ValueCountFrequency (%)
0.5980522036552431
 
0.4%
2.360876798629761
 
0.4%
(Missing)264
99.2%

Length

2022-04-03T11:46:09.294966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.359792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.5980522036552431
50.0%
2.360876798629761
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1992
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing262
Missing (%)98.5%
Memory size10.6 KiB
2.04413652420044
0.0959775224328041
3.1347963809967
3.64000010490417

Length

Max length18
Median length16
Mean length16.25
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row2.04413652420044
2nd row0.0959775224328041
3rd row3.1347963809967
4th row3.64000010490417

Common Values

ValueCountFrequency (%)
2.044136524200441
 
0.4%
0.09597752243280411
 
0.4%
3.13479638099671
 
0.4%
3.640000104904171
 
0.4%
(Missing)262
98.5%

Length

2022-04-03T11:46:09.438581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.512414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.044136524200441
25.0%
0.09597752243280411
25.0%
3.13479638099671
25.0%
3.640000104904171
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1993
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing262
Missing (%)98.5%
Memory size10.6 KiB
1.82293629646301
0.114136643707752
2.19961380958557
0.848322808742523

Length

Max length17
Median length16.5
Mean length16.5
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row1.82293629646301
2nd row0.114136643707752
3rd row2.19961380958557
4th row0.848322808742523

Common Values

ValueCountFrequency (%)
1.822936296463011
 
0.4%
0.1141366437077521
 
0.4%
2.199613809585571
 
0.4%
0.8483228087425231
 
0.4%
(Missing)262
98.5%

Length

2022-04-03T11:46:09.600177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.671988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.822936296463011
25.0%
0.1141366437077521
25.0%
2.199613809585571
25.0%
0.8483228087425231
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1994
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
0.883341312408447
2.50975871086121
1.85540354251862

Length

Max length17
Median length16
Mean length16.33333333
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row0.883341312408447
2nd row2.50975871086121
3rd row1.85540354251862

Common Values

ValueCountFrequency (%)
0.8833413124084471
 
0.4%
2.509758710861211
 
0.4%
1.855403542518621
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:46:09.758753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:09.828538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.8833413124084471
33.3%
2.509758710861211
33.3%
1.855403542518621
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1995
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)100.0%
Missing256
Missing (%)96.2%
Infinite0
Infinite (%)0.0%
Mean0.9125598159
Minimum0.05437738076
Maximum2.210038185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:09.897355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05437738076
5-th percentile0.06012955736
Q10.1926725935
median0.9096851051
Q31.346954584
95-th percentile2.048951632
Maximum2.210038185
Range2.155660804
Interquartile range (IQR)1.154281991

Descriptive statistics

Standard deviation0.7574851413
Coefficient of variation (CV)0.8300662906
Kurtosis-0.8704913353
Mean0.9125598159
Median Absolute Deviation (MAD)0.6730212085
Skewness0.4136680003
Sum9.125598159
Variance0.5737837392
MonotonicityNot monotonic
2022-04-03T11:46:09.981692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.085959888991
 
0.4%
1.0918114191
 
0.4%
0.054377380761
 
0.4%
0.067159995441
 
0.4%
1.4320023061
 
0.4%
0.93896716831
 
0.4%
0.51281070711
 
0.4%
0.88040304181
 
0.4%
2.2100381851
 
0.4%
1.8520680671
 
0.4%
(Missing)256
96.2%
ValueCountFrequency (%)
0.054377380761
0.4%
0.067159995441
0.4%
0.085959888991
0.4%
0.51281070711
0.4%
0.88040304181
0.4%
0.93896716831
0.4%
1.0918114191
0.4%
1.4320023061
0.4%
1.8520680671
0.4%
2.2100381851
0.4%
ValueCountFrequency (%)
2.2100381851
0.4%
1.8520680671
0.4%
1.4320023061
0.4%
1.0918114191
0.4%
0.93896716831
0.4%
0.88040304181
0.4%
0.51281070711
0.4%
0.085959888991
0.4%
0.067159995441
0.4%
0.054377380761
0.4%

1996
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)100.0%
Missing250
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean1.992680577
Minimum0.2081309706
Maximum5.734797955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:10.074979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2081309706
5-th percentile0.4595333375
Q10.9171798229
median1.532968104
Q32.447644949
95-th percentile5.184221625
Maximum5.734797955
Range5.526666984
Interquartile range (IQR)1.530465126

Descriptive statistics

Standard deviation1.558492773
Coefficient of variation (CV)0.7821086787
Kurtosis1.373702589
Mean1.992680577
Median Absolute Deviation (MAD)0.8921777606
Skewness1.311118259
Sum31.88288923
Variance2.428899723
MonotonicityNot monotonic
2022-04-03T11:46:10.166767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.0150902271
 
0.4%
0.91121011971
 
0.4%
2.4686133861
 
0.4%
2.440655471
 
0.4%
1.9139889481
 
0.4%
0.69413048031
 
0.4%
0.9191697241
 
0.4%
0.20813097061
 
0.4%
2.9146811961
 
0.4%
1.151947261
 
0.4%
Other values (6)6
 
2.3%
(Missing)250
94.0%
ValueCountFrequency (%)
0.20813097061
0.4%
0.54333412651
0.4%
0.69413048031
0.4%
0.91121011971
0.4%
0.9191697241
0.4%
1.0150902271
0.4%
1.1161514521
0.4%
1.151947261
0.4%
1.9139889481
0.4%
2.4182684421
0.4%
ValueCountFrequency (%)
5.7347979551
0.4%
5.0006961821
0.4%
2.9146811961
0.4%
2.4686133861
0.4%
2.440655471
0.4%
2.4320232871
0.4%
2.4182684421
0.4%
1.9139889481
0.4%
1.151947261
0.4%
1.1161514521
0.4%

1997
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)100.0%
Missing251
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean1.40488345
Minimum0.02127807587
Maximum6.136397362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:10.261510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.02127807587
5-th percentile0.06675427631
Q10.4284968823
median0.8116605878
Q31.81042707
95-th percentile3.796951461
Maximum6.136397362
Range6.115119286
Interquartile range (IQR)1.381930187

Descriptive statistics

Standard deviation1.561512311
Coefficient of variation (CV)1.111488865
Kurtosis5.788972516
Mean1.40488345
Median Absolute Deviation (MAD)0.5562487245
Skewness2.194291412
Sum21.07325175
Variance2.438320696
MonotonicityNot monotonic
2022-04-03T11:46:10.350273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.086244076491
 
0.4%
6.1363973621
 
0.4%
0.372892351
 
0.4%
1.3679093121
 
0.4%
2.7943317891
 
0.4%
1.0877100231
 
0.4%
0.42885419731
 
0.4%
1.7812058931
 
0.4%
0.021278075871
 
0.4%
0.42813956741
 
0.4%
Other values (5)5
 
1.9%
(Missing)251
94.4%
ValueCountFrequency (%)
0.021278075871
0.4%
0.086244076491
0.4%
0.372892351
0.4%
0.42813956741
0.4%
0.42885419731
0.4%
0.62110197541
0.4%
0.73227703571
0.4%
0.81166058781
0.4%
1.0877100231
0.4%
1.3679093121
0.4%
ValueCountFrequency (%)
6.1363973621
0.4%
2.7943317891
0.4%
2.5636012551
0.4%
1.8396482471
0.4%
1.7812058931
0.4%
1.3679093121
0.4%
1.0877100231
0.4%
0.81166058781
0.4%
0.73227703571
0.4%
0.62110197541
0.4%

1998
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)100.0%
Missing245
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean1.275899519
Minimum0
Maximum3.697206497
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:10.447984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07356191427
Q10.4984895289
median1.099168658
Q32.014462948
95-th percentile2.703839064
Maximum3.697206497
Range3.697206497
Interquartile range (IQR)1.515973419

Descriptive statistics

Standard deviation1.005881381
Coefficient of variation (CV)0.7883703736
Kurtosis0.03096551098
Mean1.275899519
Median Absolute Deviation (MAD)0.839626193
Skewness0.7885486525
Sum26.7938899
Variance1.011797352
MonotonicityNot monotonic
2022-04-03T11:46:10.548252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.073561914271
 
0.4%
2.5599999431
 
0.4%
0.96667271851
 
0.4%
1.1252183911
 
0.4%
0.25954246521
 
0.4%
1.0177344081
 
0.4%
1.0991686581
 
0.4%
0.49848952891
 
0.4%
2.093526841
 
0.4%
0.21229921281
 
0.4%
Other values (11)11
 
4.1%
(Missing)245
92.1%
ValueCountFrequency (%)
01
0.4%
0.073561914271
0.4%
0.21229921281
0.4%
0.25053322321
0.4%
0.25954246521
0.4%
0.49848952891
0.4%
0.72635561231
0.4%
0.82347726821
0.4%
0.96667271851
0.4%
1.0177344081
0.4%
ValueCountFrequency (%)
3.6972064971
0.4%
2.7038390641
0.4%
2.5852744581
0.4%
2.5599999431
0.4%
2.093526841
0.4%
2.0144629481
0.4%
1.6090151071
0.4%
1.3543360231
0.4%
1.1252183911
0.4%
1.1231756211
0.4%

1999
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct22
Distinct (%)95.7%
Missing243
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean1.668348204
Minimum0
Maximum9.979640007
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:10.658725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.009226353467
Q10.2028678954
median0.7657024264
Q31.947401404
95-th percentile6.560769701
Maximum9.979640007
Range9.979640007
Interquartile range (IQR)1.744533509

Descriptive statistics

Standard deviation2.412532557
Coefficient of variation (CV)1.446060572
Kurtosis6.388432857
Mean1.668348204
Median Absolute Deviation (MAD)0.6430719495
Skewness2.460439604
Sum38.37200868
Variance5.82031334
MonotonicityNot monotonic
2022-04-03T11:46:11.038945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
02
 
0.8%
0.63756293061
 
0.4%
0.76570242641
 
0.4%
2.5748035911
 
0.4%
0.31350907681
 
0.4%
1.1815809011
 
0.4%
1.2201975581
 
0.4%
0.097945295271
 
0.4%
3.1175479891
 
0.4%
1.4087743761
 
0.4%
Other values (12)12
 
4.5%
(Missing)243
91.4%
ValueCountFrequency (%)
02
0.8%
0.092263534671
0.4%
0.097945295271
0.4%
0.11453872171
0.4%
0.14974248411
0.4%
0.25599330661
0.4%
0.31350907681
0.4%
0.63756293061
0.4%
0.65766346451
0.4%
0.66932761671
0.4%
ValueCountFrequency (%)
9.9796400071
0.4%
6.8833165171
0.4%
3.6578483581
0.4%
3.1175479891
0.4%
2.5748035911
0.4%
2.4860284331
0.4%
1.4087743761
0.4%
1.2201975581
0.4%
1.1815809011
0.4%
1.0658078191
0.4%

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)100.0%
Missing223
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean1.763224756
Minimum0.05701898038
Maximum7.748924732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:11.154635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.05701898038
5-th percentile0.2245549083
Q10.5737961531
median1.199345827
Q32.513256788
95-th percentile4.644188428
Maximum7.748924732
Range7.691905752
Interquartile range (IQR)1.939460635

Descriptive statistics

Standard deviation1.53387479
Coefficient of variation (CV)0.8699258475
Kurtosis4.363312748
Mean1.763224756
Median Absolute Deviation (MAD)0.9390039742
Skewness1.70951309
Sum75.81866451
Variance2.352771872
MonotonicityNot monotonic
2022-04-03T11:46:11.278333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.7662572861
 
0.4%
0.51960855721
 
0.4%
1.0112298731
 
0.4%
2.8913164141
 
0.4%
0.61347872021
 
0.4%
1.4612988231
 
0.4%
2.4030597211
 
0.4%
1.1993458271
 
0.4%
0.2896682621
 
0.4%
1.3351436851
 
0.4%
Other values (33)33
 
12.4%
(Missing)223
83.8%
ValueCountFrequency (%)
0.057018980381
0.4%
0.20127210021
0.4%
0.22057858111
0.4%
0.26034185291
0.4%
0.2896682621
0.4%
0.30000001191
0.4%
0.33309933541
0.4%
0.4000000061
0.4%
0.41991233831
0.4%
0.51960855721
0.4%
ValueCountFrequency (%)
7.7489247321
0.4%
4.7662572861
0.4%
4.7336330411
0.4%
3.8391869071
0.4%
3.266101361
0.4%
2.9722101691
0.4%
2.8913164141
0.4%
2.7214956281
0.4%
2.683052541
0.4%
2.6436095241
0.4%

2001
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)100.0%
Missing238
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean1.536503969
Minimum0
Maximum5.923856258
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:11.398982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08562851027
Q10.3970398977
median1.105305374
Q32.533508241
95-th percentile4.085861635
Maximum5.923856258
Range5.923856258
Interquartile range (IQR)2.136468343

Descriptive statistics

Standard deviation1.512173775
Coefficient of variation (CV)0.9841652249
Kurtosis1.135067362
Mean1.536503969
Median Absolute Deviation (MAD)0.9484280944
Skewness1.204229331
Sum43.02211114
Variance2.286669524
MonotonicityNot monotonic
2022-04-03T11:46:11.504699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.55613487961
 
0.4%
01
 
0.4%
0.14696776871
 
0.4%
0.38815957311
 
0.4%
1.2580460311
 
0.4%
2.0438239571
 
0.4%
1.2707915311
 
0.4%
0.084627501671
 
0.4%
3.2283842561
 
0.4%
2.9718585011
 
0.4%
Other values (18)18
 
6.8%
(Missing)238
89.5%
ValueCountFrequency (%)
01
0.4%
0.084627501671
0.4%
0.087487526241
0.4%
0.096804030241
0.4%
0.14696776871
0.4%
0.2000000031
0.4%
0.38815957311
0.4%
0.4000000061
0.4%
0.41875356441
0.4%
0.48823371531
0.4%
ValueCountFrequency (%)
5.9238562581
0.4%
4.4299626351
0.4%
3.4468169211
0.4%
3.2283842561
0.4%
3.0570390221
0.4%
2.9718585011
0.4%
2.6881399151
0.4%
2.481964351
0.4%
2.3287000661
0.4%
2.0438239571
0.4%

2002
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)100.0%
Missing230
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean1.570268618
Minimum0.03304109722
Maximum5.959089756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:11.620418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.03304109722
5-th percentile0.05120713916
Q10.4577818662
median1.157467902
Q32.524099827
95-th percentile4.47968924
Maximum5.959089756
Range5.926048659
Interquartile range (IQR)2.066317961

Descriptive statistics

Standard deviation1.505880955
Coefficient of variation (CV)0.9589957654
Kurtosis1.698547573
Mean1.570268618
Median Absolute Deviation (MAD)0.8919041678
Skewness1.356278854
Sum56.52967025
Variance2.267677451
MonotonicityNot monotonic
2022-04-03T11:46:11.733116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.1855269671
 
0.4%
2.8845398431
 
0.4%
1.3410557511
 
0.4%
3.0527002811
 
0.4%
0.81254893541
 
0.4%
0.075591929261
 
0.4%
0.045454122131
 
0.4%
1.3086069821
 
0.4%
1.5504618881
 
0.4%
0.62586247921
 
0.4%
Other values (26)26
 
9.8%
(Missing)230
86.5%
ValueCountFrequency (%)
0.033041097221
0.4%
0.045454122131
0.4%
0.05312481151
0.4%
0.057461630551
0.4%
0.075591929261
0.4%
0.11498003451
0.4%
0.1855269671
0.4%
0.2000000031
0.4%
0.33112746481
0.4%
0.51
0.4%
ValueCountFrequency (%)
5.9590897561
0.4%
5.6362881661
0.4%
4.0941562651
0.4%
3.1954863071
0.4%
3.0527002811
0.4%
2.9914596081
0.4%
2.9525074961
0.4%
2.8845398431
0.4%
2.680619241
0.4%
2.4719266891
0.4%

2003
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)100.0%
Missing229
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean2.210120854
Minimum0.02713847533
Maximum15.18728256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:11.852798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.02713847533
5-th percentile0.1133018248
Q10.6979252696
median1.257572055
Q32.573663712
95-th percentile6.588199997
Maximum15.18728256
Range15.16014409
Interquartile range (IQR)1.875738442

Descriptive statistics

Standard deviation2.970046354
Coefficient of variation (CV)1.34383889
Kurtosis10.9799083
Mean2.210120854
Median Absolute Deviation (MAD)0.7416296601
Skewness3.098176685
Sum81.7744716
Variance8.821175347
MonotonicityNot monotonic
2022-04-03T11:46:11.969989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
15.187282561
 
0.4%
10.644826891
 
0.4%
0.79971003531
 
0.4%
3.2324426171
 
0.4%
0.61489140991
 
0.4%
1.1105138061
 
0.4%
1.4551355841
 
0.4%
0.13049460951
 
0.4%
1.3254227641
 
0.4%
5.5740432741
 
0.4%
Other values (27)27
 
10.2%
(Missing)229
86.1%
ValueCountFrequency (%)
0.027138475331
0.4%
0.044530685991
0.4%
0.13049460951
0.4%
0.15184889731
0.4%
0.2000000031
0.4%
0.22200590371
0.4%
0.51594239471
0.4%
0.60000002381
0.4%
0.61489140991
0.4%
0.69792526961
0.4%
ValueCountFrequency (%)
15.187282561
0.4%
10.644826891
0.4%
5.5740432741
0.4%
4.9083681111
0.4%
3.9005186561
0.4%
3.5025293831
0.4%
3.389250041
0.4%
3.2324426171
0.4%
2.5929548741
0.4%
2.5736637121
0.4%

2004
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)100.0%
Missing226
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean1.455989631
Minimum0
Maximum7.496141434
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:12.090695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09830770604
Q10.4467027336
median0.8962266743
Q31.955083877
95-th percentile4.083387566
Maximum7.496141434
Range7.496141434
Interquartile range (IQR)1.508381143

Descriptive statistics

Standard deviation1.538910676
Coefficient of variation (CV)1.056951673
Kurtosis5.127396249
Mean1.455989631
Median Absolute Deviation (MAD)0.7392063662
Skewness1.996087912
Sum58.23958524
Variance2.36824607
MonotonicityNot monotonic
2022-04-03T11:46:12.212370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2.5280566221
 
0.4%
2.6209301951
 
0.4%
0.5875334741
 
0.4%
0.8388327361
 
0.4%
1.8077310321
 
0.4%
01
 
0.4%
0.066154092551
 
0.4%
1.5143318181
 
0.4%
1.1758553981
 
0.4%
2.0709011551
 
0.4%
Other values (30)30
 
11.3%
(Missing)226
85.0%
ValueCountFrequency (%)
01
0.4%
0.066154092551
0.4%
0.10000000151
0.4%
0.10286066681
0.4%
0.1343802811
0.4%
0.13770958781
0.4%
0.17633102831
0.4%
0.17780645191
0.4%
0.33037596941
0.4%
0.42998367551
0.4%
ValueCountFrequency (%)
7.4961414341
0.4%
4.7035598751
0.4%
4.0507469181
0.4%
3.7698295121
0.4%
3.3241643911
0.4%
2.8773345951
0.4%
2.6209301951
0.4%
2.5280566221
0.4%
2.0709011551
0.4%
2.021539451
0.4%

2005
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct56
Distinct (%)98.2%
Missing209
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean1.729601628
Minimum0
Maximum7.062345505
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:12.344047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0921402052
Q10.5024527907
median1.097877383
Q32.414920807
95-th percentile4.940556431
Maximum7.062345505
Range7.062345505
Interquartile range (IQR)1.912468016

Descriptive statistics

Standard deviation1.593171776
Coefficient of variation (CV)0.9211206498
Kurtosis1.938245941
Mean1.729601628
Median Absolute Deviation (MAD)0.7612595856
Skewness1.409173034
Sum98.58729282
Variance2.538196307
MonotonicityNot monotonic
2022-04-03T11:46:12.475272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
0.8%
2.2770571711
 
0.4%
1.8524081711
 
0.4%
0.69430941341
 
0.4%
1.5130718951
 
0.4%
0.91704046731
 
0.4%
0.16584795711
 
0.4%
1.9557878971
 
0.4%
0.85247570281
 
0.4%
2.4123868941
 
0.4%
Other values (46)46
 
17.3%
(Missing)209
78.6%
ValueCountFrequency (%)
02
0.8%
0.060701020061
0.4%
0.10000000151
0.4%
0.16584795711
0.4%
0.18784289061
0.4%
0.22019603851
0.4%
0.33661779761
0.4%
0.34295666221
0.4%
0.35571983461
0.4%
0.4000000061
0.4%
ValueCountFrequency (%)
7.0623455051
0.4%
6.1830639841
0.4%
5.376724721
0.4%
4.8315143591
0.4%
4.4973950391
0.4%
3.5931441781
0.4%
3.3979842661
0.4%
3.2811880111
0.4%
3.2112457751
0.4%
3.0764079091
0.4%

2006
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)97.6%
Missing225
Missing (%)84.6%
Infinite0
Infinite (%)0.0%
Mean1.417323416
Minimum0
Maximum5.907929897
Zeros2
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:12.607491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09251830727
Q10.6556360722
median0.8361976743
Q31.78730619
95-th percentile3.8390975
Maximum5.907929897
Range5.907929897
Interquartile range (IQR)1.131670117

Descriptive statistics

Standard deviation1.402512853
Coefficient of variation (CV)0.9895503292
Kurtosis2.715331496
Mean1.417323416
Median Absolute Deviation (MAD)0.5015992522
Skewness1.719877727
Sum58.11026007
Variance1.967042304
MonotonicityNot monotonic
2022-04-03T11:46:12.724341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
02
 
0.8%
2.4436171051
 
0.4%
0.092518307271
 
0.4%
0.6946423651
 
0.4%
0.65570235251
 
0.4%
1.3377969261
 
0.4%
0.97538602351
 
0.4%
1.787306191
 
0.4%
0.68932032591
 
0.4%
1.5359489921
 
0.4%
Other values (30)30
 
11.3%
(Missing)225
84.6%
ValueCountFrequency (%)
02
0.8%
0.092518307271
0.4%
0.10000000151
0.4%
0.20534503461
0.4%
0.24899975961
0.4%
0.4256062211
0.4%
0.42785429951
0.4%
0.45362725851
0.4%
0.58790212871
0.4%
0.65563607221
0.4%
ValueCountFrequency (%)
5.9079298971
0.4%
5.4399118421
0.4%
3.83909751
0.4%
3.8264255521
0.4%
3.5675878521
0.4%
3.0494828221
0.4%
2.6996617321
0.4%
2.4436171051
0.4%
1.9097365141
0.4%
1.8314657211
0.4%

2007
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)100.0%
Missing223
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean1.553077672
Minimum0.02192341536
Maximum5.880308151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:12.846949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.02192341536
5-th percentile0.06952227801
Q10.3162085712
median1.15720737
Q32.290771961
95-th percentile4.819802809
Maximum5.880308151
Range5.858384736
Interquartile range (IQR)1.97456339

Descriptive statistics

Standard deviation1.522805345
Coefficient of variation (CV)0.9805081693
Kurtosis1.568143035
Mean1.553077672
Median Absolute Deviation (MAD)0.8674448729
Skewness1.400124889
Sum66.78233991
Variance2.31893612
MonotonicityNot monotonic
2022-04-03T11:46:12.967473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.06560297311
 
0.4%
1.595293881
 
0.4%
3.3405323031
 
0.4%
3.5194554331
 
0.4%
1.4808853861
 
0.4%
0.10479602221
 
0.4%
0.037825182081
 
0.4%
0.28976249691
 
0.4%
1.8212431671
 
0.4%
1.2755780221
 
0.4%
Other values (33)33
 
12.4%
(Missing)223
83.8%
ValueCountFrequency (%)
0.021923415361
0.4%
0.037825182081
0.4%
0.06560297311
0.4%
0.10479602221
0.4%
0.14866000411
0.4%
0.2000000031
0.4%
0.21669822931
0.4%
0.26314470171
0.4%
0.26366835831
0.4%
0.28976249691
0.4%
ValueCountFrequency (%)
5.8803081511
0.4%
5.8486490251
0.4%
4.9040060041
0.4%
4.0619740491
0.4%
3.5194554331
0.4%
3.3405323031
0.4%
2.9437074661
0.4%
2.7899260521
0.4%
2.6615750791
0.4%
2.4192242621
0.4%

2008
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)100.0%
Missing228
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean1.811572259
Minimum0.1106027365
Maximum7.898897648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:13.090143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1106027365
5-th percentile0.2456431411
Q10.5712633729
median1.228690386
Q32.807575107
95-th percentile4.126865792
Maximum7.898897648
Range7.788294911
Interquartile range (IQR)2.236311734

Descriptive statistics

Standard deviation1.656482183
Coefficient of variation (CV)0.9143892412
Kurtosis3.357108256
Mean1.811572259
Median Absolute Deviation (MAD)0.8892710507
Skewness1.556038376
Sum68.83974583
Variance2.743933223
MonotonicityNot monotonic
2022-04-03T11:46:13.210821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1.6267638211
 
0.4%
3.5564112661
 
0.4%
0.73080199961
 
0.4%
2.8548941611
 
0.4%
1.1205943821
 
0.4%
0.30738586191
 
0.4%
0.63517421481
 
0.4%
1.5635576251
 
0.4%
2.3608939651
 
0.4%
2.6656179431
 
0.4%
Other values (28)28
 
10.5%
(Missing)228
85.7%
ValueCountFrequency (%)
0.11060273651
0.4%
0.2000000031
0.4%
0.25369781261
0.4%
0.29731568691
0.4%
0.29760879281
0.4%
0.30738586191
0.4%
0.42302283641
0.4%
0.51352769141
0.4%
0.54635018111
0.4%
0.56168448931
0.4%
ValueCountFrequency (%)
7.8988976481
0.4%
4.4156904221
0.4%
4.075896741
0.4%
3.9672203061
0.4%
3.5975828171
0.4%
3.5564112661
0.4%
3.5312321191
0.4%
3.4752883911
0.4%
3.1091868881
0.4%
2.8548941611
0.4%

2009
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct46
Distinct (%)100.0%
Missing220
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean1.901336172
Minimum0.1182854399
Maximum9.54515934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:13.340474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1182854399
5-th percentile0.2012979202
Q10.5253085345
median1.144813061
Q32.6826666
95-th percentile6.056320667
Maximum9.54515934
Range9.4268739
Interquartile range (IQR)2.157358065

Descriptive statistics

Standard deviation2.137569058
Coefficient of variation (CV)1.124245722
Kurtosis4.732451267
Mean1.901336172
Median Absolute Deviation (MAD)0.7973700017
Skewness2.100168193
Sum87.46146392
Variance4.569201478
MonotonicityNot monotonic
2022-04-03T11:46:13.472111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3.3365705011
 
0.4%
0.22135943171
 
0.4%
0.21258477871
 
0.4%
8.920058251
 
0.4%
2.4673407081
 
0.4%
1.3514380461
 
0.4%
1.0105077031
 
0.4%
1.6594846251
 
0.4%
1.2791184191
 
0.4%
2.7014081481
 
0.4%
Other values (36)36
 
13.5%
(Missing)220
82.7%
ValueCountFrequency (%)
0.11828543991
0.4%
0.13084438441
0.4%
0.2000000031
0.4%
0.20519167181
0.4%
0.21258477871
0.4%
0.22135943171
0.4%
0.33142536881
0.4%
0.36346074941
0.4%
0.40538558361
0.4%
0.4796504081
0.4%
ValueCountFrequency (%)
9.545159341
0.4%
8.920058251
0.4%
6.1589107511
0.4%
5.7485504151
0.4%
3.6752481461
0.4%
3.5807244781
0.4%
3.4971952441
0.4%
3.3943257331
0.4%
3.3365705011
0.4%
3.2803676131
0.4%

2010
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)100.0%
Missing203
Missing (%)76.3%
Infinite0
Infinite (%)0.0%
Mean1.514264895
Minimum0.004647844471
Maximum7.850300312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:13.602733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.004647844471
5-th percentile0.05357425138
Q10.3378708512
median0.9735930562
Q32.135945678
95-th percentile4.492474079
Maximum7.850300312
Range7.845652468
Interquartile range (IQR)1.798074827

Descriptive statistics

Standard deviation1.610472866
Coefficient of variation (CV)1.063534439
Kurtosis3.125943406
Mean1.514264895
Median Absolute Deviation (MAD)0.7735930532
Skewness1.669025726
Sum95.39868837
Variance2.593622851
MonotonicityNot monotonic
2022-04-03T11:46:13.736913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3474607471
 
0.4%
3.7927467821
 
0.4%
0.21416656671
 
0.4%
2.1310644151
 
0.4%
0.72031444311
 
0.4%
0.12564025821
 
0.4%
0.095655843621
 
0.4%
0.76835471391
 
0.4%
5.367167951
 
0.4%
0.36611956361
 
0.4%
Other values (53)53
 
19.9%
(Missing)203
76.3%
ValueCountFrequency (%)
0.0046478444711
0.4%
0.009120418691
0.4%
0.029296061021
0.4%
0.052686344831
0.4%
0.061565410351
0.4%
0.095655843621
0.4%
0.095868155361
0.4%
0.096818201241
0.4%
0.12564025821
0.4%
0.1481694431
0.4%
ValueCountFrequency (%)
7.8503003121
0.4%
5.367167951
0.4%
5.3474607471
0.4%
4.5245981221
0.4%
4.2033576971
0.4%
3.8769721981
0.4%
3.7927467821
0.4%
3.6863706111
0.4%
3.6228907111
0.4%
3.4628195761
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)100.0%
Missing227
Missing (%)85.3%
Infinite0
Infinite (%)0.0%
Mean2.030749752
Minimum0.03095917962
Maximum11.90197563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:13.864012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.03095917962
5-th percentile0.1907423943
Q10.5794085711
median1.285012484
Q32.729897022
95-th percentile5.085183001
Maximum11.90197563
Range11.87101645
Interquartile range (IQR)2.150488451

Descriptive statistics

Standard deviation2.275345405
Coefficient of variation (CV)1.120445984
Kurtosis8.755282671
Mean2.030749752
Median Absolute Deviation (MAD)0.8352929354
Skewness2.579272805
Sum79.19924033
Variance5.177196711
MonotonicityNot monotonic
2022-04-03T11:46:13.986710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3.3217840191
 
0.4%
0.201119111
 
0.4%
1.8625054361
 
0.4%
0.48680761461
 
0.4%
1.2850124841
 
0.4%
1.417504431
 
0.4%
3.3110785481
 
0.4%
2.3940329551
 
0.4%
0.49944040181
 
0.4%
0.1935805381
 
0.4%
Other values (29)29
 
10.9%
(Missing)227
85.3%
ValueCountFrequency (%)
0.030959179621
0.4%
0.1651991011
0.4%
0.1935805381
0.4%
0.201119111
0.4%
0.20498082041
0.4%
0.30000001191
0.4%
0.38160207871
0.4%
0.44971954821
0.4%
0.48680761461
0.4%
0.49944040181
0.4%
ValueCountFrequency (%)
11.901975631
0.4%
7.0937829021
0.4%
4.8620052341
0.4%
4.8128294941
0.4%
4.5537896161
0.4%
3.5954103471
0.4%
3.3217840191
0.4%
3.3110785481
0.4%
3.2268285751
0.4%
3.0657610891
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)100.0%
Missing222
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean1.770947297
Minimum0.07564873993
Maximum6.716030121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:14.110379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.07564873993
5-th percentile0.1743748873
Q10.6178766936
median1.27301538
Q32.094619572
95-th percentile4.876067662
Maximum6.716030121
Range6.640381381
Interquartile range (IQR)1.476742879

Descriptive statistics

Standard deviation1.681815325
Coefficient of variation (CV)0.9496698897
Kurtosis1.332446832
Mean1.770947297
Median Absolute Deviation (MAD)0.7090320289
Skewness1.412312792
Sum77.92168108
Variance2.828502786
MonotonicityNot monotonic
2022-04-03T11:46:14.233130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1.0059000251
 
0.4%
4.202025891
 
0.4%
3.2595674991
 
0.4%
0.63126212361
 
0.4%
0.48384863141
 
0.4%
0.84394431111
 
0.4%
0.2875964941
 
0.4%
0.78771847491
 
0.4%
4.4172244071
 
0.4%
1.4751660821
 
0.4%
Other values (34)34
 
12.8%
(Missing)222
83.5%
ValueCountFrequency (%)
0.075648739931
0.4%
0.10444647821
0.4%
0.16985280811
0.4%
0.2000000031
0.4%
0.23866151271
0.4%
0.2875964941
0.4%
0.31991934781
0.4%
0.38916000721
0.4%
0.41576325891
0.4%
0.48384863141
0.4%
ValueCountFrequency (%)
6.7160301211
0.4%
6.3000001911
0.4%
4.94539691
0.4%
4.4832019811
0.4%
4.4172244071
0.4%
4.202025891
0.4%
3.8964397911
0.4%
3.5870532991
0.4%
3.2595674991
0.4%
3.2201447491
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)100.0%
Missing226
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean1.890362638
Minimum0.06645663828
Maximum6.792263031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:14.355803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.06645663828
5-th percentile0.200271491
Q10.63243258
median1.429274797
Q32.201690733
95-th percentile5.322640276
Maximum6.792263031
Range6.725806393
Interquartile range (IQR)1.569258153

Descriptive statistics

Standard deviation1.716369607
Coefficient of variation (CV)0.9079578555
Kurtosis1.42876481
Mean1.890362638
Median Absolute Deviation (MAD)0.8076530695
Skewness1.419178805
Sum75.61450554
Variance2.945924629
MonotonicityNot monotonic
2022-04-03T11:46:14.482435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1.7302166221
 
0.4%
0.15999999641
 
0.4%
2.1214632991
 
0.4%
0.6432434321
 
0.4%
1.383685351
 
0.4%
1.3551087381
 
0.4%
2.3751146791
 
0.4%
2.01323391
 
0.4%
0.66081339121
 
0.4%
0.29126873611
 
0.4%
Other values (30)30
 
11.3%
(Missing)226
85.0%
ValueCountFrequency (%)
0.066456638281
0.4%
0.15999999641
0.4%
0.20239104331
0.4%
0.29126873611
0.4%
0.40358155971
0.4%
0.43286356331
0.4%
0.48044490811
0.4%
0.48449957371
0.4%
0.51
0.4%
0.60000002381
0.4%
ValueCountFrequency (%)
6.7922630311
0.4%
6.4389357571
0.4%
5.2638878821
0.4%
4.8713212011
0.4%
4.3210554121
0.4%
3.9844055181
0.4%
3.761337281
0.4%
3.5586662291
0.4%
2.4408202171
0.4%
2.3751146791
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)100.0%
Missing222
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean1.901930987
Minimum0.08075411618
Maximum9.102802277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:14.608129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.08075411618
5-th percentile0.2345983543
Q10.5271445066
median1.216597021
Q32.439799607
95-th percentile5.866026521
Maximum9.102802277
Range9.02204816
Interquartile range (IQR)1.9126551

Descriptive statistics

Standard deviation1.927686621
Coefficient of variation (CV)1.013541834
Kurtosis3.686043812
Mean1.901930987
Median Absolute Deviation (MAD)0.8063880056
Skewness1.80213329
Sum83.68496345
Variance3.715975709
MonotonicityNot monotonic
2022-04-03T11:46:14.729796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.3360069991
 
0.4%
0.51945626741
 
0.4%
0.40306347611
 
0.4%
2.9462101461
 
0.4%
9.1028022771
 
0.4%
2.2709960941
 
0.4%
1.2467147111
 
0.4%
1.186479331
 
0.4%
1.9337620741
 
0.4%
0.59572023151
 
0.4%
Other values (34)34
 
12.8%
(Missing)222
83.5%
ValueCountFrequency (%)
0.080754116181
0.4%
0.12076421081
0.4%
0.22228015961
0.4%
0.30440145731
0.4%
0.3360069991
0.4%
0.3488006891
0.4%
0.4000000061
0.4%
0.40263840561
0.4%
0.40306347611
0.4%
0.41735455391
0.4%
ValueCountFrequency (%)
9.1028022771
0.4%
6.1702446941
0.4%
6.0336656571
0.4%
4.9160714151
0.4%
4.1590495111
0.4%
4.0981788641
0.4%
3.7884197241
0.4%
3.7795701031
0.4%
3.3708963391
0.4%
3.0245165821
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct57
Distinct (%)98.3%
Missing208
Missing (%)78.2%
Infinite0
Infinite (%)0.0%
Mean1.953650143
Minimum0.005823389627
Maximum7.787830353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:14.858428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005823389627
5-th percentile0.1083552085
Q10.5
median1.393106163
Q32.678911626
95-th percentile5.269114304
Maximum7.787830353
Range7.782006963
Interquartile range (IQR)2.178911626

Descriptive statistics

Standard deviation1.884482489
Coefficient of variation (CV)0.9645956805
Kurtosis1.381498774
Mean1.953650143
Median Absolute Deviation (MAD)0.9758007079
Skewness1.357504539
Sum113.3117083
Variance3.551274251
MonotonicityNot monotonic
2022-04-03T11:46:14.991073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52
 
0.8%
2.4737496381
 
0.4%
0.22369217871
 
0.4%
2.7472989561
 
0.4%
3.5842947961
 
0.4%
0.77786129711
 
0.4%
0.7451747061
 
0.4%
0.31883051991
 
0.4%
4.5540628431
 
0.4%
2.1358766561
 
0.4%
Other values (47)47
 
17.7%
(Missing)208
78.2%
ValueCountFrequency (%)
0.0058233896271
0.4%
0.028285250071
0.4%
0.042525425551
0.4%
0.1199722291
0.4%
0.22369217871
0.4%
0.22370822731
0.4%
0.26520481711
0.4%
0.30521038171
0.4%
0.31883051991
0.4%
0.3595284821
0.4%
ValueCountFrequency (%)
7.7878303531
0.4%
7.582869531
0.4%
5.2847609521
0.4%
5.266353131
0.4%
5.1872444151
0.4%
5.0739068981
0.4%
4.9261760711
0.4%
4.5540628431
0.4%
4.0389981271
0.4%
3.893427611
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)100.0%
Missing215
Missing (%)80.8%
Infinite0
Infinite (%)0.0%
Mean1.939822355
Minimum0
Maximum9.18357563
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:15.396629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1485020295
Q10.6607609391
median1.293421745
Q32.210226297
95-th percentile5.724146843
Maximum9.18357563
Range9.18357563
Interquartile range (IQR)1.549465358

Descriptive statistics

Standard deviation2.033768224
Coefficient of variation (CV)1.048430141
Kurtosis3.829157619
Mean1.939822355
Median Absolute Deviation (MAD)0.7959084511
Skewness1.912526444
Sum98.9309401
Variance4.136213191
MonotonicityNot monotonic
2022-04-03T11:46:15.527308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.6139044761
 
0.4%
0.23062883321
 
0.4%
0.16699026531
 
0.4%
0.9139167071
 
0.4%
1.0209492441
 
0.4%
0.18999999761
 
0.4%
2.102242471
 
0.4%
1.018926741
 
0.4%
1.3156939741
 
0.4%
0.96602290871
 
0.4%
Other values (41)41
 
15.4%
(Missing)215
80.8%
ValueCountFrequency (%)
01
0.4%
0.11108952021
0.4%
0.13072189691
0.4%
0.16628216211
0.4%
0.16699026531
0.4%
0.18999999761
0.4%
0.23062883321
0.4%
0.28100699191
0.4%
0.32112008331
0.4%
0.36360546951
0.4%
ValueCountFrequency (%)
9.183575631
0.4%
8.4479150771
0.4%
5.7482047081
0.4%
5.7000889781
0.4%
5.6139044761
0.4%
4.0667777061
0.4%
3.8194217681
0.4%
3.7999804021
0.4%
3.7723858361
0.4%
3.5634622571
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing224
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean2.617433037
Minimum0
Maximum9.737969398
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:15.652507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2660866648
Q10.8893471807
median1.822640181
Q33.807608724
95-th percentile7.000646067
Maximum9.737969398
Range9.737969398
Interquartile range (IQR)2.918261543

Descriptive statistics

Standard deviation2.319100283
Coefficient of variation (CV)0.8860208649
Kurtosis0.9092085344
Mean2.617433037
Median Absolute Deviation (MAD)1.13004601
Skewness1.211967277
Sum109.9321875
Variance5.378226122
MonotonicityNot monotonic
2022-04-03T11:46:15.777179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.885004521
 
0.4%
4.5332570081
 
0.4%
3.0185656551
 
0.4%
0.058495208621
 
0.4%
3.4682855611
 
0.4%
1.9046131371
 
0.4%
0.68518835311
 
0.4%
0.65158492331
 
0.4%
0.88579624891
 
0.4%
01
 
0.4%
Other values (32)32
 
12.0%
(Missing)224
84.2%
ValueCountFrequency (%)
01
0.4%
0.058495208621
0.4%
0.25849387051
0.4%
0.41034975651
0.4%
0.50881952051
0.4%
0.65158492331
0.4%
0.68518835311
0.4%
0.69999998811
0.4%
0.71676665541
0.4%
0.87407577041
0.4%
ValueCountFrequency (%)
9.7379693981
0.4%
7.0943059921
0.4%
7.0251760481
0.4%
6.5345764161
0.4%
6.0967617031
0.4%
5.885004521
0.4%
5.4699573521
0.4%
5.0100526811
0.4%
4.5332570081
0.4%
4.0473818781
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)100.0%
Missing240
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean2.040794575
Minimum0.08951681107
Maximum12.4773035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:15.884887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.08951681107
5-th percentile0.3177780882
Q10.8389756382
median1.254602253
Q32.760262728
95-th percentile3.995655477
Maximum12.4773035
Range12.38778669
Interquartile range (IQR)1.92128709

Descriptive statistics

Standard deviation2.413870426
Coefficient of variation (CV)1.182809115
Kurtosis14.51064006
Mean2.040794575
Median Absolute Deviation (MAD)0.8993716836
Skewness3.439432128
Sum53.06065895
Variance5.826770435
MonotonicityNot monotonic
2022-04-03T11:46:15.995594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.3340067861
 
0.4%
1.2313892841
 
0.4%
0.81676876541
 
0.4%
2.1929476261
 
0.4%
0.34103071691
 
0.4%
2.4338829521
 
0.4%
0.31002721191
 
0.4%
3.044699431
 
0.4%
0.57999998331
 
0.4%
1.2778152231
 
0.4%
Other values (16)16
 
6.0%
(Missing)240
90.2%
ValueCountFrequency (%)
0.089516811071
0.4%
0.31002721191
0.4%
0.34103071691
0.4%
0.36943042281
0.4%
0.51
0.4%
0.57999998331
0.4%
0.81676876541
0.4%
0.90559625631
0.4%
0.94140356781
0.4%
1.063357831
0.4%
ValueCountFrequency (%)
12.47730351
0.4%
4.0942072871
0.4%
3.7000000481
0.4%
3.0999999051
0.4%
3.0673196321
0.4%
3.044699431
0.4%
2.8690559861
0.4%
2.4338829521
0.4%
2.4275417331
0.4%
2.1929476261
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)100.0%
Missing253
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean1.412539645
Minimum0.1306083202
Maximum4.920944214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:46:16.093333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1306083202
5-th percentile0.2163007319
Q10.7560901046
median1.122995377
Q31.299999952
95-th percentile4.04419775
Maximum4.920944214
Range4.790335894
Interquartile range (IQR)0.5439098477

Descriptive statistics

Standard deviation1.346320816
Coefficient of variation (CV)0.9531207292
Kurtosis3.392893713
Mean1.412539645
Median Absolute Deviation (MAD)0.366905272
Skewness1.895140205
Sum18.36301538
Variance1.812579741
MonotonicityNot monotonic
2022-04-03T11:46:16.192041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.60000002381
 
0.4%
0.80539107321
 
0.4%
0.80000001191
 
0.4%
1.1229953771
 
0.4%
3.4597001081
 
0.4%
1.8999999761
 
0.4%
4.9209442141
 
0.4%
0.13060832021
 
0.4%
1.141036631
 
0.4%
1.2999999521
 
0.4%
Other values (3)3
 
1.1%
(Missing)253
95.1%
ValueCountFrequency (%)
0.13060832021
0.4%
0.27342900631
0.4%
0.60000002381
0.4%
0.75609010461
0.4%
0.80000001191
0.4%
0.80539107321
0.4%
1.1229953771
0.4%
1.141036631
0.4%
1.1528205871
0.4%
1.2999999521
0.4%
ValueCountFrequency (%)
4.9209442141
0.4%
3.4597001081
0.4%
1.8999999761
0.4%
1.2999999521
0.4%
1.1528205871
0.4%
1.141036631
0.4%
1.1229953771
0.4%
0.80539107321
0.4%
0.80000001191
0.4%
0.75609010461
0.4%

2020
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
0.600000023841858
0.899999976158142
1.73390829563141

Length

Max length17
Median length17
Mean length16.66666667
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row0.600000023841858
2nd row0.899999976158142
3rd row1.73390829563141

Common Values

ValueCountFrequency (%)
0.6000000238418581
 
0.4%
0.8999999761581421
 
0.4%
1.733908295631411
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:46:16.298788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:46:16.368598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.6000000238418581
33.3%
0.8999999761581421
33.3%
1.733908295631411
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-03T11:46:03.257996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:05.779489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.040920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.310084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:12.779492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.039473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.550315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:19.785654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.290304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.527579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.058007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.395918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:31.949356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.177650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:36.737934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.095688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.620018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.008075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.632030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:48.909209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.429322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.650892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.266981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.448399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:00.933682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.337754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:05.861299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.123671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.393890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:12.869223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.129232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.638671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:19.877441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.378696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.622326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.155362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.480661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.040300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.272399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:36.819720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.178467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.710746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.105125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.735752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:48.989026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.533045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.739687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.356741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.538150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:01.036823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.430506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:05.943052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.214429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.759941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:12.963015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.215004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.729396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:19.961185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.464468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.707070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.236151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.572415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.128307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.353181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:36.910450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.284447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.814469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.211046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.820526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:49.091417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.621808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.838424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.454479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.617938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:01.134274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.526278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:06.028850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.309498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.848704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:13.053756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.301801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.811205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:20.056957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.553810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.804291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.328447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.654226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.211836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.445933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:37.001773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.357222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.902262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.295357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.901338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:49.173170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.710570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.925221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.539224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.698915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:01.226537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.603045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:06.111629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.396265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.931484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:13.166454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.385574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.888969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:20.149244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.642087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.903028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.419741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.735007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.294285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.544177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:37.103550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.439003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.992022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.384598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.990096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:49.261444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.799361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:54.011986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.627986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.779457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:01.319792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.692827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:06.198369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.479015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:11.020273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:13.259102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.469349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.974765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:20.238067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.732844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.992758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.514457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.824768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.388011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.631943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:37.201294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.531756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:42.087802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:44.483840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:47.075351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:49.353198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.888120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:54.104253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.717774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.865225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:01.418528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.784102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:06.290152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:08.574759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:11.099065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:13.335896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:15.829397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:18.075497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:20.315860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.821577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:25.083515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:27.601868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.957385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:32.467817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.714750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:37.289124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.615532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:42.185575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-03T11:45:46.361753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:48.643920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.159074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.379210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:55.975775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.162155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:00.345931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:02.981707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:05.330477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:07.862401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.130566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:12.585158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:14.872947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.361686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:19.607132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.115711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.357007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:26.867885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.219077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:31.766130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.000126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:36.561416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:38.915135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.443489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:43.821473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.449520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:48.731690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.243819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.470740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.070030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.255876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:00.428709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.077479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:05.422230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:07.958114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:10.224344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:12.680728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:14.961680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:17.461414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:19.699885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:22.198551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:24.446766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:26.962598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:29.312142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:31.857404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:34.098862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:36.649148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:39.001968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:41.535215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:43.914225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:46.543272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:48.821449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:51.334576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:53.565061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:56.172755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:45:58.352646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:00.835943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:46:03.167211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-03T11:46:16.551103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-03T11:46:17.405629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-03T11:46:18.265027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-03T11:46:19.038502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-03T11:46:06.145386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-03T11:46:07.031052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-03T11:46:07.852401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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